In [1]:
import graphlab

Load music data

In [2]:
song_data = graphlab.SFrame('song_data.gl/')

This non-commercial license of GraphLab Create for academic use is assigned to ssq6554@gmail.com and will expire on October 10, 2017.

[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: C:\Users\Beginner\AppData\Local\Temp\graphlab_server_1477729626.log.0

Explore data

In [3]:
song_data.head()
Out[3]:
user_id song_id listen_count title artist
b80344d063b5ccb3212f76538
f3d9e43d87dca9e ...		 SOAKIMP12A8C130995 1 The Cove Jack Johnson
b80344d063b5ccb3212f76538
f3d9e43d87dca9e ...		 SOBBMDR12A8C13253B 2 Entre Dos Aguas Paco De Lucia
b80344d063b5ccb3212f76538
f3d9e43d87dca9e ...		 SOBXHDL12A81C204C0 1 Stronger Kanye West
b80344d063b5ccb3212f76538
f3d9e43d87dca9e ...		 SOBYHAJ12A6701BF1D 1 Constellations Jack Johnson
b80344d063b5ccb3212f76538
f3d9e43d87dca9e ...		 SODACBL12A8C13C273 1 Learn To Fly Foo Fighters
b80344d063b5ccb3212f76538
f3d9e43d87dca9e ...		 SODDNQT12A6D4F5F7E 5 Apuesta Por El Rock 'N'
Roll ...		 Héroes del Silencio
b80344d063b5ccb3212f76538
f3d9e43d87dca9e ...		 SODXRTY12AB0180F3B 1 Paper Gangsta Lady GaGa
b80344d063b5ccb3212f76538
f3d9e43d87dca9e ...		 SOFGUAY12AB017B0A8 1 Stacked Actors Foo Fighters
b80344d063b5ccb3212f76538
f3d9e43d87dca9e ...		 SOFRQTD12A81C233C0 1 Sehr kosmisch Harmonia
b80344d063b5ccb3212f76538
f3d9e43d87dca9e ...		 SOHQWYZ12A6D4FA701 1 Heaven's gonna burn your
eyes ...		 Thievery Corporation
feat. Emiliana Torrini ...
song
The Cove - Jack Johnson
Entre Dos Aguas - Paco De
Lucia ...
Stronger - Kanye West
Constellations - Jack
Johnson ...
Learn To Fly - Foo
Fighters ...
Apuesta Por El Rock 'N'
Roll - Héroes del ...
Paper Gangsta - Lady GaGa
Stacked Actors - Foo
Fighters ...
Sehr kosmisch - Harmonia
Heaven's gonna burn your
eyes - Thievery ...
[10 rows x 6 columns]
In [4]:
graphlab.canvas.set_target('ipynb')
In [5]:
song_data['song'].show()
In [6]:
len(song_data)
Out[6]:
1116609

count number of users

In [8]:
users = song_data['user_id'].unique()
In [9]:
len(users)
Out[9]:
66346

create a song recommender

In [10]:
train_data,test_data = song_data.random_split(.8,seed=0)

Simple popularity-based recommender

In [25]:
popularity_model = graphlab.popularity_recommender.create(train_data,
                                                         user_id='user_id',
                                                         item_id='song')

Recsys training: model = popularity
Warning: Ignoring columns song_id, listen_count, title, artist;
    To use one of these as a target column, set target = 
    and use a method that allows the use of a target.
Preparing data set.
    Data has 893580 observations with 66085 users and 9952 items.
    Data prepared in: 1.50062s
893580 observations to process; with 9952 unique items.

Use the popularity model to make some predictions

In [26]:
popularity_model.recommend(users=[users[0]])
Out[26]:
user_id song score rank
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Sehr kosmisch - Harmonia 4754.0 1
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Undo - Björk 4227.0 2
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 You're The One - Dwight
Yoakam ...		 3781.0 3
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Dog Days Are Over (Radio
Edit) - Florence + The ...		 3633.0 4
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Revelry - Kings Of Leon 3527.0 5
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Horn Concerto No. 4 in E
flat K495: II. Romance ...		 3161.0 6
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Secrets - OneRepublic 3148.0 7
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Fireflies - Charttraxx
Karaoke ...		 2532.0 8
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Tive Sim - Cartola 2521.0 9
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Drop The World - Lil
Wayne / Eminem ...		 2053.0 10
[10 rows x 4 columns]
In [27]:
popularity_model.recommend(users=[users[1]])
Out[27]:
user_id song score rank
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Sehr kosmisch - Harmonia 4754.0 1
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Undo - Björk 4227.0 2
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 You're The One - Dwight
Yoakam ...		 3781.0 3
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Dog Days Are Over (Radio
Edit) - Florence + The ...		 3633.0 4
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Revelry - Kings Of Leon 3527.0 5
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Horn Concerto No. 4 in E
flat K495: II. Romance ...		 3161.0 6
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Secrets - OneRepublic 3148.0 7
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Hey_ Soul Sister - Train 2538.0 8
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Fireflies - Charttraxx
Karaoke ...		 2532.0 9
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Tive Sim - Cartola 2521.0 10
[10 rows x 4 columns]
In [28]:
popularity_model.recommend(users=[users[0],users[1]])
Out[28]:
user_id song score rank
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Sehr kosmisch - Harmonia 4754.0 1
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Undo - Björk 4227.0 2
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 You're The One - Dwight
Yoakam ...		 3781.0 3
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Dog Days Are Over (Radio
Edit) - Florence + The ...		 3633.0 4
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Revelry - Kings Of Leon 3527.0 5
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Horn Concerto No. 4 in E
flat K495: II. Romance ...		 3161.0 6
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Secrets - OneRepublic 3148.0 7
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Fireflies - Charttraxx
Karaoke ...		 2532.0 8
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Tive Sim - Cartola 2521.0 9
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Drop The World - Lil
Wayne / Eminem ...		 2053.0 10
[20 rows x 4 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.

Build a song recommender with personalization

In [29]:
personalized_model = graphlab.item_similarity_recommender.create(train_data,
                                                                user_id='user_id',
                                                                item_id='song')

Recsys training: model = item_similarity
Warning: Ignoring columns song_id, listen_count, title, artist;
    To use one of these as a target column, set target = 
    and use a method that allows the use of a target.
Preparing data set.
    Data has 893580 observations with 66085 users and 9952 items.
    Data prepared in: 1.34803s
Training model from provided data.
Gathering per-item and per-user statistics.
+--------------------------------+------------+
| Elapsed Time (Item Statistics) | % Complete |
+--------------------------------+------------+
| 0us                            | 4.5        |
| 31.2ms                         | 100        |
+--------------------------------+------------+
Setting up lookup tables.
Processing data in one pass using dense lookup tables.
+-------------------------------------+------------------+-----------------+
| Elapsed Time (Constructing Lookups) | Total % Complete | Items Processed |
+-------------------------------------+------------------+-----------------+
| 211.607ms                           | 0                | 0               |
| 1.82s                               | 100              | 9952            |
+-------------------------------------+------------------+-----------------+
Finalizing lookup tables.
Generating candidate set for working with new users.
Finished training in 1.91543s

Applying the personalized model to make song recommendations

In [30]:
personalized_model.recommend(users=[users[0]])
Out[30]:
user_id song score rank
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Cuando Pase El Temblor -
Soda Stereo ...		 0.0194504536115 1
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Fireflies - Charttraxx
Karaoke ...		 0.0144737317012 2
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Love Is A Losing Game -
Amy Winehouse ...		 0.0142865960415 3
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Marry Me - Train 0.014133471709 4
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Secrets - OneRepublic 0.013591665488 5
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Sehr kosmisch - Harmonia 0.0133987894425 6
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Te Hacen Falta Vitaminas
- Soda Stereo ...		 0.0129302831796 7
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 OMG - Usher featuring
will.i.am ...		 0.0127778282532 8
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 Y solo se me ocurre
amarte (Unplugged) - ...		 0.0123411279458 9
c66c10a9567f0d82ff31441a9
fd5063e5cd9dfe8 ...		 No Dejes Que... -
Caifanes ...		 0.0121042499175 10
[10 rows x 4 columns]
In [31]:
personalized_model.recommend(users=[users[1]])
Out[31]:
user_id song score rank
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Riot In Cell Block Number
Nine - Dr Feelgood ...		 0.0374999940395 1
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Sei Lá Mangueira -
Elizeth Cardoso ...		 0.0331632643938 2
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 The Stallion - Ween 0.0322580635548 3
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Rain - Subhumans 0.0314159244299 4
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 West One (Shine On Me) -
The Ruts ...		 0.0306771993637 5
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Back Against The Wall -
Cage The Elephant ...		 0.0301204770803 6
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Life Less Frightening -
Rise Against ...		 0.0284431129694 7
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 A Beggar On A Beach Of
Gold - Mike And The ...		 0.0230024904013 8
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Audience Of One - Rise
Against ...		 0.0193938463926 9
279292bb36dbfc7f505e36ebf
038c81eb1d1d63e ...		 Blame It On The Boogie -
The Jacksons ...		 0.0189873427153 10
[10 rows x 4 columns]
In [32]:
personalized_model.recommend(users=[users[2]])
Out[32]:
user_id song score rank
c067c22072a17d33310d7223d
7b79f819e48cf42 ...		 Grind With Me (Explicit
Version) - Pretty Ricky ...		 0.0459424376488 1
c067c22072a17d33310d7223d
7b79f819e48cf42 ...		 There Goes My Baby -
Usher ...		 0.0331920742989 2
c067c22072a17d33310d7223d
7b79f819e48cf42 ...		 Panty Droppa [Intro]
(Album Version) - Trey ...		 0.0318566203117 3
c067c22072a17d33310d7223d
7b79f819e48cf42 ...		 Nobody (Featuring Athena
Cage) (LP Version) - ...		 0.0278467655182 4
c067c22072a17d33310d7223d
7b79f819e48cf42 ...		 Youth Against Fascism -
Sonic Youth ...		 0.0262914180756 5
c067c22072a17d33310d7223d
7b79f819e48cf42 ...		 Nice & Slow - Usher 0.0239639401436 6
c067c22072a17d33310d7223d
7b79f819e48cf42 ...		 Making Love (Into The
Night) - Usher ...		 0.0238176941872 7
c067c22072a17d33310d7223d
7b79f819e48cf42 ...		 Naked - Marques Houston 0.0228925704956 8
c067c22072a17d33310d7223d
7b79f819e48cf42 ...		 I.nner Indulgence -
DESTRUCTION ...		 0.0220767498016 9
c067c22072a17d33310d7223d
7b79f819e48cf42 ...		 Love Lost (Album Version)
- Trey Songz ...		 0.0204497694969 10
[10 rows x 4 columns]
In [33]:
personalized_model.get_similar_items(['With Or Without You - U2'])
Out[33]:
song similar score rank
With Or Without You - U2 I Still Haven't Found
What I'm Looking For ...		 0.042857170105 1
With Or Without You - U2 Hold Me_ Thrill Me_ Kiss
Me_ Kill Me - U2 ...		 0.0337349176407 2
With Or Without You - U2 Window In The Skies - U2 0.0328358411789 3
With Or Without You - U2 Vertigo - U2 0.0300751924515 4
With Or Without You - U2 Sunday Bloody Sunday - U2 0.0271317958832 5
With Or Without You - U2 Bad - U2 0.0251798629761 6
With Or Without You - U2 A Day Without Me - U2 0.0237154364586 7
With Or Without You - U2 Another Time Another
Place - U2 ...		 0.0203251838684 8
With Or Without You - U2 Walk On - U2 0.0202020406723 9
With Or Without You - U2 Get On Your Boots - U2 0.0196850299835 10
[10 rows x 4 columns]
In [34]:
personalized_model.get_similar_items(['Chan Chan (Live) - Buena Vista Social Club'])
Out[34]:
song similar score rank
Chan Chan (Live) - Buena
Vista Social Club ...		 Murmullo - Buena Vista
Social Club ...		 0.188118815422 1
Chan Chan (Live) - Buena
Vista Social Club ...		 La Bayamesa - Buena Vista
Social Club ...		 0.18719214201 2
Chan Chan (Live) - Buena
Vista Social Club ...		 Amor de Loca Juventud -
Buena Vista Social Club ...		 0.184834122658 3
Chan Chan (Live) - Buena
Vista Social Club ...		 Diferente - Gotan Project 0.0214592218399 4
Chan Chan (Live) - Buena
Vista Social Club ...		 Mistica - Orishas 0.0205761194229 5
Chan Chan (Live) - Buena
Vista Social Club ...		 Hotel California - Gipsy
Kings ...		 0.0193049907684 6
Chan Chan (Live) - Buena
Vista Social Club ...		 Nací Orishas - Orishas 0.0191571116447 7
Chan Chan (Live) - Buena
Vista Social Club ...		 Gitana - Willie Colon 0.018796980381 8
Chan Chan (Live) - Buena
Vista Social Club ...		 Le Moulin - Yann Tiersen 0.018796980381 9
Chan Chan (Live) - Buena
Vista Social Club ...		 Criminal - Gotan Project 0.0187793374062 10
[10 rows x 4 columns]

Quantitative comparison between the models

In [36]:
model_performance = graphlab.compare(test_data, [popularity_model, personalized_model], user_sample=0.05)
graphlab.show_comparison(model_performance,[popularity_model, personalized_model])

compare_models: using 2931 users to estimate model performance
PROGRESS: Evaluate model M0

recommendations finished on 1000/2931 queries. users per second: 4492.3
recommendations finished on 2000/2931 queries. users per second: 6131.06
Precision and recall summary statistics by cutoff
+--------+-----------------+------------------+
| cutoff |  mean_precision |   mean_recall    |
+--------+-----------------+------------------+
|   1    | 0.0245649948823 | 0.00612161730381 |
|   2    | 0.0228590924599 |  0.011369683427  |
|   3    | 0.0213806436938 | 0.0162475749733  |
|   4    | 0.0199590583419 | 0.0203157375624  |
|   5    |  0.018219037871 | 0.0240192850479  |
|   6    | 0.0172864778801 | 0.0271066559961  |
|   7    | 0.0164254033241 | 0.0300735637993  |
|   8    | 0.0159501876493 | 0.0334411087226  |
|   9    | 0.0154289396869 | 0.0357355720969  |
|   10   | 0.0149095871716 | 0.0382195347395  |
+--------+-----------------+------------------+
[10 rows x 3 columns]

PROGRESS: Evaluate model M1

recommendations finished on 1000/2931 queries. users per second: 7429.31
recommendations finished on 2000/2931 queries. users per second: 8136.46
Precision and recall summary statistics by cutoff
+--------+-----------------+-----------------+
| cutoff |  mean_precision |   mean_recall   |
+--------+-----------------+-----------------+
|   1    |  0.183555100648 | 0.0608062953764 |
|   2    |  0.155237120437 | 0.0961782441184 |
|   3    |  0.136131013306 |  0.12197044303  |
|   4    |  0.121801432958 |  0.141331643072 |
|   5    |  0.109996588195 |  0.157212564531 |
|   6    |  0.101956101444 |  0.173869876096 |
|   7    | 0.0950918750305 |  0.18783266044  |
|   8    | 0.0894745820539 |  0.202043918305 |
|   9    | 0.0846885780356 |  0.214101382052 |
|   10   | 0.0799044694643 |  0.223207688574 |
+--------+-----------------+-----------------+
[10 rows x 3 columns]

Model compare metric: precision_recall
In [ ]: